Project opportunities

As a non-exhaustive list, OxyKodit offers the following types of projects. Each can be set up as a separate project, depending on the current phase and requirements of your specific use-case:

1. AI proof-of-concept: Identify ways to integrate Artificial Intelligence into your business and domain by implementing a tangible PoC using public data and/or confidential in-house data.
2. Production-grade code: Ensure your code base is ready for production by performing code reviews, extended quality assurance and making sure there is a robust unit testing framework in place.
3. Open-source maintenance: Implement features, fix bugs, review PRs, organize issues, engage the community and answer discussion threads.

2. Ensure your code base is ready for production

• Perform rigorous testing and assess the current quality of both the data and the code base.
• Identify structural errors (if any) in the dataset and/or annotation guidelines.
• Make the code more robust and more performant in terms of speed and memory usage.
• Tune the algorithms in terms of predictive performance to make them more accurate and reliable.
• Iterate on both data and the ML models.
• Make sure there is a robust unit testing framework in place.
• Example projects I have worked on:
• Performed an in-depth review of an existing code base to identify opportunities to increase performance, both in terms of efficiencies (memory usage, throughput) and accuracy (correctness of the results).
• Experimented with various hyperparameter tuning experiments as well as different architectures to optimize F-score of supervised ML models.
• Performed a critical evaluation of existing tools to recognize mentions of cell lines in text, and developed two new annotated datasets to further boost development of NLP algorithms in this domain.
• Analysed an NER dataset from a customer and identified structural ambiguities and conflicts. Refined the annotation guidelines accordingly and trained new models on the curated dataset, obtaining much more robust and accurate results.

3. Open-source maintenance

• Extend the code base with new functionality according to specific feature requests.
• Fix self-encountered and user-reported bugs.
• Review PRs with a keen eye for detail and ensuring maintainability and robustness of the code base.
• Organizing and answering issues and discussion threads on Github.
• Engaging with the community at large and providing guidance on contributing to open-source.
• Example repositories I have worked on:
• The NLP library spaCy and its ecosystem, including spacy-llm, spacy-transformers and the underlying ML library Thinc
• The popular web framework FastAPI for building high-performance APIs
• A research tool called Diffany to infer and visualize differential networks
• Typer: a library for building CLI applications with Python easily
• neuralcoref: a neural network framework for performing Coreference Resolution
• A partially vibe-coded hobby project to support language learning through fun vocabulary exercises: LexiKon